Users campaign for peaking energy usage

ABSTRACT

Aspects of the subject technology relate to a system that analyzes customers&#39; AMI load curves, identifies evening peak users as defined by their load curves, and provides Energy Efficiency (EE) advice related to their periods of high use. For example, identified high evening users can be sent an email with normative comparisons on evening load use, along with tips to reduce energy usage. Other aspects relate to the additional targets/communications. Aspects of the subject technology relate to categorizing a user&#39;s energy consumption tendencies based on a user&#39;s load curve and providing customized content based on the user&#39;s category. By taking into consideration the user&#39;s actual energy consumption patterns, the system may be able to provide more relevant content to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Non-Provisionalapplication Ser. No. 14/581,105 filed Dec. 23, 2014, issued on Nov. 5,2019 as U.S. Pat. No. 10,467,249 and claims the benefit of priority toU.S. Provisional Application Ser. No. 62/034,535, filed Aug. 7, 2014,entitled “EVENING PEAKING USERS CAMPAIGN,” which is hereby incorporatedby reference in its entirety.

BACKGROUND

Segmentation and targeting of users is usually done based on demographicinformation (e.g., age, household size, income level, rent or own, etc.)associated with the user, information about the user's property (e.g.,the size of the property, the type of fuels used, thermostat set points,etc.), or the location of the user. This information can be used toguess how users consume electricity, select personalized content basedon that guess, and provide the personalized content to the user.

SUMMARY

The subject technology includes computer-implemented method forimplementing an evening peak user campaign, comprising: aggregatingconsumption data for a plurality of users, the consumption datacorresponding with an amount of energy resource consumption for each ofthe respective plurality of users; generating, from the consumptiondata, at least one load curve for the plurality of users; identifying,from the at least one load curve, a subset of the plurality of usersthat are peak users that consume more resources during a first timeperiod than a second time period; generating a plurality of use reportsfor the peak users, wherein each of the use reports provides a visualdisplay of the consumption data for each of the peak users and energyefficiency advice related to consumption during the first time period;and providing one or more of the use reports to one or more of the peakusers to reduce the amount of energy resource consumption for the one ormore peak users.

The subject technology provides a computing device for implementing anevening peak user campaign, the computing device comprising: at leastone processor; and memory storing instructions that, when executed bythe at least one processor, cause the computing device to: aggregateconsumption data for a plurality of users, the consumption datacorresponding with an amount of energy resource consumption for each ofthe respective plurality of users; generate, from the consumption data,load curves for the plurality of users; and identify, based at least inpart on the load curves, a new user that is indicated as a peak user,the new user being a different user other than the plurality of users,the new user consuming more resources during a first time period than asecond time period.

The subject technology further provides a non-transitory computerreadable storage medium storing instructions for implementing an eveningpeak user campaign on a computing device, the instructions when executedby a processor causing the processor to: aggregate consumption data fora plurality of users, the consumption data corresponding with an amountof energy resource consumption for each of the respective plurality ofusers; identify, based on the consumption data, usage patterns for theplurality of users; identify, from the usage patterns, a subset of theplurality of users that are peak users that consume more resourcesduring a first time period than a second time period; generate aplurality of use reports for the peak users, wherein each of the usereports provides a visual display of the consumption data for each ofthe peak users and energy efficiency advice related to consumptionduring the first time period; and provide one or more of the use reportsto one or more of the peak users to reduce the amount of energy resourceconsumption for the one or more peak users.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following description, reference is made to the followingfigures, and in which are shown by way of illustration specificembodiments in which the subject technology may be practiced. It is tobe understood that other embodiments may be utilized and changes may bemade without departing from the scope of the subject technology.

FIG. 1 illustrates an example of an energy usage alert system, accordingto certain aspects of the subject technology.

FIG. 2 illustrates an example load curve for tracking energy usage by auser over a period of time, according to certain aspects of the subjecttechnology.

FIG. 3 illustrates different examples of load curve archetypes forrepresenting a category of energy usage by a user over a period of time,according to certain aspects of the subject technology.

FIG. 4A illustrates an example of an energy usage alert notificationprovided to a utility customer, according to certain aspects of thesubject technology.

FIG. 4B illustrates an example of an energy usage alert notificationprovided to a utility customer, according to certain aspects of thesubject technology.

FIG. 5 illustrates a flowchart of an example process for the energyusage alert system described in FIG. 1 to provide.

FIG. 6 illustrates an example of an environment for implementing aspectsin accordance with various embodiments.

FIG. 7 illustrates an example of a system for energy usage alerts,according to certain aspects of the subject technology.

FIG. 8 illustrates an example configuration of components of a computingdevice, according to certain aspects of the subject technology.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Different consumers tend to consume resources, such as power, duringdifferent parts of the day. For example, some consumers consumeresources primarily during the evening, whereas others consume resourcesprimarily during the morning or mid-day. To be more relevant,recommendations regarding how to reduce resource consumption should betailored based on when the consumer primarily consumes resources.

Aspects of the subject technology relate to systems and methods foranalyzing customers' advanced metering infrastructure (AMI) load curves,identifying evening peak users as defined by their load curves, andproviding Energy Efficiency (EE) advice related to periods of high use.For example, identified high evening users can be sent an email withnormative comparisons of evening load use, along with tips to reduceenergy usage. As used herein, the phrase “load curve” may refer to agraphical representation (and the underlying data that is graphicallyrepresented) that illustrates the variation in a user or utilitycustomer's energy consumption over a specific time period. Other aspectsof the subject technology relate to additional targets/communications.For example, identifying electric vehicle users based on their loadcurves and then providing the identified users with advice for when tocharge (e.g., times with lowest rates—midnight to 6 AM) is contemplatedby the embodiment of the subject technology described herein.

In one embodiment, a system of the subject technology can be configuredto aggregate consumption data for a plurality of users. The consumptiondata can correspond with an amount of energy resource consumption foreach of the respective plurality of users. AMI load curves can begenerated from the consumption data and used to identify a subset of theplurality of users that are evening peak users that consume resourcesduring evening hours. The system can generate a plurality of use reportsfor the evening peak users that provide a visual display of theconsumption data for each of the evening peak users and energyefficiency advice related to consumption during evening hours. Thegenerated reports can then be provided to one or more of the eveningpeak users to reduce the amount of energy resource consumption for theone or more evening peak users. As used herein, evening hours maycorresponding to a span of hours during nighttime which, in someexamples, may correspond to 6 pm-12 am or some subset therein. However,it is appreciated that evening hours may correspond to an hour(s) before6 pm (e.g., when the evening starts at 4 pm or 5 pm) and an hour(s)after 12 am (e.g., after midnight).

Aspects of the subject technology can further relate to categorizing auser's energy consumption tendencies based on a user's load curve andproviding customized content based on the user's category. By takinginto consideration the user's actual energy consumption patterns, thesystem may be able to provide more relevant content and/orrecommendations to the user.

A system may be configured to retrieve energy usage data for a user(e.g., AMI meter data) for a period of time and generate a load curvefor the user for that period of time. For example, the load curve may befor a day, a week, a month, a season, or a year. In some cases, multiplesets of energy usage data may be used to generate the load curve for adefined period of time. For example, the energy usage data for the past12 weeks may be aggregated (e.g., averaged, taking a weighted average,eliminating outlier weeks, etc.) and used to generate a weekly loadcurve for the user. Additional load curves (e.g., a daily load curve forweekend days, a daily load curve for weekdays or a particular weekday, amonthly load curve, load curves for the spring or summer or fall orwinter season, etc.) may similarly be generated for the user.

The system may compare the one or more load curves for the user withload curve archetypes to see which load curve archetypes most closelymatch the load curves for the user. As used herein, a load curvearchetype is a load curve that represents a type of user based on theirusage patterns tracked during a period of time (e.g., a day, etc.). Anexample load curve archetype may include one that categorizes the useras one that has peak usage in the evening (e.g., “evening peak user”),one that has peak usage in the morning (e.g., “morning peak user”), orone that has peak usage during mid-day. The user may then be associatedwith an energy use profile (e.g., corresponding to a type of user) basedon the load curve archetypes that the user's load curves most closelyresemble. The category of the user may then be used to segment the userand provide the user with targeted content.

The “system” described herein may be implemented on a server, a climatecontrol device, or on a computing device in communication with theclimate control device. The climate control device may represent one ormore of a thermostat, an application running on a computing device, or acomputing device coupled to the thermostat depending on implementation.The term “usage” described herein refers to a quantity of use, a costassociated with the use, or a quantified metric representing the use orcost. The term “actual energy usage” described herein refers to a meterreading or a usage reading. The term “commodity” described herein refersto a utility-based commodity, such as electricity, water, and naturalgas, which are consumable finite resources delivered to a dwelling or acommercial structure. The term “component of a property” describedherein refers to a component associated with the property that is ableto consume a commodity. One example of a component of a property may bea heating, ventilation and air conditioning (HVAC) system that controlsthe climate within the property using electricity, natural gas, and/oranother commodity. The component may relate to one or more of a centralheating device, a central air conditioning and heating system, anappliance, an electronic device, water heating system, a powergenerating device, a ventilation system, or an air filtration system.

FIG. 1 illustrates an example of an energy usage alert system 100,according to certain aspects of the subject technology. The energy usagealert system 100 includes a utility management system 104 and a billingmanagement system 108. The utility management system 104 is coupled toutility customers 101 via monitoring devices 102 and climate controldevices 103. The utility management system 104 includes usage data 105,a billing operation module 106 and load curve data 107. In anembodiment, the usage data 105 and/or the load curve data 107 are storedin respective databases, data stores, or any appropriate storage means,computer-readable medium, or mechanism for storing corresponding dataand information therein. The billing management system 108 includes abudget module 109, a rate module 110, a forecast module 111, a monitormodule 112, a report module 113, a recommendation module 114, and a usersegmentation module 116. The billing management system 108 may conveyinformation targeted to one or more of the utility customers 101 a-101 nover communication channels 115.

The utility management system 104 stores usage data in the usage data105. The usage data is associated with one or more commodities consumedby the utility customers 101. The usage data may include usageinformation corresponding to usage of at least one of the one or morecommodities for multiple utility customers (e.g., utility customers 101a, 101 b . . . 101 n). The usage-information may include past usageinformation of the commodity during at least one of completed billingperiod and a current usage of the at least one of the one or morecommodities during a completed portion of a current billing period. Theusage data for a utility customer may be obtained from a correspondingmonitoring device on a scheduled basis, periodic basis or anon-scheduled basis. The monitoring devices (e.g., monitoring devices102 a, 102 b . . . 102 n) may relate to an advanced meteringinfrastructure (AMI). In this respect, the monitoring devices may besmart meters or, at least in part, include smart meter functionality formeasuring electrical, water and/or natural gas consumption in theproperty associated with the corresponding utility customer. Forexample, the usage data may consist of usage information correspondingto the property in its entirety such that usage information relating toone or more components in the property is disaggregated by the utilitymanagement system 104 and/or the billing management system 108. Inanother example, the usage data 105 may contain information from non-AMIsources such as an analog meter, which is provided to the utilitymanagement system 104 by other means. In an aspect, the utilitymanagement system 104 stores and forwards the usage data to the billingmanagement system 108 for usage alert processing. The utility managementsystem 104 may forward the usage data to the billing management system108 for storage and usage alert processing. The utility managementsystem 104 described herein may refer to a utility company or an offsitethird party service provider that is interfaced with the utilitycompany.

The utility management system 104 stores load curve information in theload curve database 107. The load curve information may be based on theusage data 105. For example, the billing operation module 106 may obtainthe usage data 105 to determine a load curve over a time period for thecorresponding utility customer. A more detailed description of the loadcurve determination will be described in FIG. 2.

The budget module 109 may determine a target budget for the currentbilling period based on the usage data. In an aspect, the budget module109 may include a budget advisor, which is an automated system for atleast determining one or more candidate budget targets. The rate module110 may store a local copy of a rate schedule associated with the feesfor commodities provided by the utility company. The rate module 110 maybe configured to obtain the rate schedule, associated with the currentbilling period, from the utility company or energy provider. Theforecast module 111 may be configured to forecast the projected use ofenergy by the utility customers 101 a-101 n based on the correspondingusage data. The forecast module 111 may include an algorithm used todetermine the projected use information using rate of use informationand billing period information. The monitor module 112 may include aninterface to the monitoring devices 102 a-102 n to obtain the usage datadirectly and/or include an interface with the utility management system104 to receive the usage data for further processing by one or morecomponents of the billing management system 108 (e.g., projected useinformation, rate of use information, target budgets). The report module113 may be configured to generate a usage alert notification, and causethe usage alert notification to be sent to one or more of the utilitycustomers 101 a-101 n based on one or more reporting conditions (e.g.,projected bill exceeding target budget, current billing period ended,utility customer inquiry, etc.) through the communication channels 115.The recommendation module 114 may be configured to provide one or morerecommendations to one or more of utility customers 101 a, 101 b to 101n for reducing energy usage. The user segementation module 116 maycategorize one or more utility customers 101 a, 101 b to 101 n intodifferent types of users based at least in part on the load curve data107 and/or the usage data 105.

The communication channels 115 may carry alert notifications to theutility customers 101 a-101 n over a wired and/or a wirelesscommunication. Further such notifications may be provided though email,Short Message Service (SMS) or interactive voice response (IVR)channels, mobile device communications, and physical mail delivery. Inan aspect, the billing management system 108 sends the alertnotifications in a broadcast and/or multicast signal to the utilitycustomers 101 a-101 n via the climate control devices 103 a-103 n. Thebilling management system 108 may specifically target one or more of theutility customers 101 a-101 n, and send a personalized alertnotification over a unicast signal. The communication channels 115 maybe configured to interface to a customer's mobile device, a customer'scomputing device, another server or system, a data exchange interface ofa cellular network, other networks, a smart meter (e.g., the monitoringdevices 102 a-102 n) and/or a thermostat (e.g., the climate controldevice 103 a-103 n). It is contemplated that other devices and networksmay be interfaced with the communication channels 115 and still bewithin the scope of the subject technology. In an embodiment, a channelsuch as physical delivery of correspondence (e.g., physical mail) may beutilized as well to provide messages and/or energy alerts to one or morecustomers.

In operation, the energy usage alert system 100 allows for the analysisof usage data 105 associated with a user to determine a load curve forthe user, which may be stored as information in the load curve data 107.The load curve may represent the user's energy consumption over aspecified time period (e.g., an amount of minutes, an amount of hours,an amount of days, an amount of weeks, an amount of months, etc.).

Aspects of the subject technology relate to systems and methods foranalyzing customers' load curves (e.g., advanced metering infrastructure(AMI) load curves), identifying evening peak users as defined by theirload curves, and providing Energy Efficiency (EE) advice related toperiods of high use. For example, identified high evening users can besent an email with normative comparisons of evening load use, along withtips to reduce energy usage. Although AMI data is used as an example, itis appreciated that other meter data with small enough granularity maybe used.

FIG. 2 illustrates an example load curve 200 for tracking energy usageby a user over a period of time, according to certain aspects of thesubject technology. The aforementioned system energy usage alert system100 may retrieve the usage data 105 corresponding to the energy usage ofa user to generate a load curve representing energy consumption over atime period. As mentioned before, the usage data 105 may be AMI meterdata. For example, the load curve may correspond to a time period of aday, a week, a month, a season, or a year, or any combination of theaforementioned. In some cases, multiple sets of energy usage data (e.g.,consumption data) may be used to generate the load curve for a period oftime. For example, the energy usage data for the past 12 weeks may beaggregated (e.g., averaged, taking a weighted average, eliminatingoutlier weeks, etc.) and used to generate a weekly load curve for theuser. In an embodiment, the energy usage data when tracked over a periodof time may form a usage pattern in which a load curve may bedetermined. Additional load curves (e.g., a daily load curve for weekenddays, a daily load curve for weekdays or a particular weekday, a monthlyload curve, etc.) may also be generated for the user.

In an example for determining a load curve, for a time period of a day,a user's energy usage may be tracked during the day for each hour, and apercentage of the total usage (e.g., a sum of usage for the respective24 hours of the day) is determined for each hour of energy usage,producing 24 data points in the load curve. For these 24 data points,the y-axis represents the percentage of total usage at a respectivehour, and the x-axis represents each of the hours in the day. Otherexamples may include separately or conjunctively tracking an averageamount of usage for each hour, a standard deviation of usage for eachhour, or baseload for each hour.

As illustrated in the example of FIG. 2, the load curve 200 isgraphically represented as a percentage of total energy usage of a timeperiod on the y-axis over the same time period on the x-axis. The timeperiod shown for the load curve 200 in FIG. 2 is 24 hours of a day alongthe x-axis. The load curve 200 includes a peak portion 230 on the rightportion of the graphical representation that indicates that the loadcurve 200 corresponds to a category of user that has peak energy usagein the evening. Further, the load curve 200 has a pattern in which thepercentage of total energy usage is far less than the peak portion 230.Although, one example of a load curve for a user that has peak energyusage in the evening is shown in FIG. 2, it is appreciated that otherload curves may correspond to such a user.

Although the above example discusses generating load curves, it isappreciated that the subject technology may determine usage patterns(e.g., without a corresponding visual representation) based at least inpart on the energy usage data or consumption data and then determine acategory of user based on such usage patterns. It is contemplated thatthe subject technology may utilize either a load curve or a usagepattern to categorize a utility customer or user in accordance toembodiments described herein.

FIG. 3 illustrates different examples of load curve archetypes forrepresenting a category of energy usage by a user over a period of time,according to certain aspects of the subject technology.

The system may be configured to generate a number of load curvearchetypes by retrieving energy usage data from one or more utilityproviders. As discussed before, the energy usage data includes AMI smartmeter data that provides energy consumption data in defined intervals(e.g., every 5 minutes, 15 minutes, every hour, etc.). In an embodiment,a clustering algorithm (e.g., a k-means algorithm) may be run on theretrieved energy usage data and used to find a number of similar users.The k-means clustering algorithm, in one example, can partition n numberof observations into k number of clusters in which each observationbelongs to the cluster with the nearest mean, serving as a prototype ofthe cluster. This results in a partitioning of the data space intoVoronoi cells. A Voronoi cell may refer to a region, among multipleregions of a partitioned plane (e.g., the data space), in which theregions are partitioned based on “closeness” to points in a specificsubset of the plane. Each Voronoi cell therefore may correspond to atype of user based on the users' retrieved energy usage data.

Further, load curve archetypes may also be determined through publiclyavailable resources of daily energy usage, actual customer usageprofiles, or guesses at what load curves may look like. Publiclyavailable resources may include sources like the Energy InformationAdministration (EIA), or Independent Service Operators (ISOs) andRegional Transmission Organizations (RTOs).

In an embodiment, load curve archetypes may be determined from customerusage profiles. Examples of this may be clustering load curves and usingthe resulting centroids as archetypes. K-means clustering is a usefulalgorithm to discover archetypes, though other clustering algorithms mayalso be used. With a k-means algorithm, the number of archetypes may beselected and the k-means algorithm discovers the centroids (archetypes)that best partition the given load curves into the chosen number ofgroups. The centers or medians of those groups (e.g., mean or medianusage at each time-interval for each group) is chosen as an archetype inan example.

As illustrated in FIG. 3, load curve archetypes 310, 320 and 330 eachrepresent a respective archetype for a user with peak energy usage inthe evening (e.g., a “peak evening user” as used herein). Each of theload curve archetypes 310,320 and 330 include peak portions at an areaof the curve representing a time period corresponding to hours in theday during the evening, while also having data points in the remainingportion of the curve with lower (and sometimes significantly lower)energy usage. In an embodiment, the load curve archetypes 310, 320 and330 each represent a proportion of daily usage in each hour of the day,with hourly usage being the average usage in each hour for a number ofdays. For example, the average usage at midnight, 1 am, 2 am, etc., isdetermined, and then each average is divided by the sum of all theaverages. By doing so, the load curves with different magnitudes andsimilar usage patterns may be normalized to have the same scale.Although example of load archetypes that may correspond with peakevening users are shown in FIG. 3, it is appreciated that other loadarchetypes for identifying different types of peak energy usage may beprovided. For example load archetypes for energy usage for weekends,weekdays, seasons, etc., may be provided to further categorize a utilitycustomer.

As mentioned before, the system may compare one or more load curves forthe user with load curve archetypes to see which load curve archetypesmost closely match the load curves for the user. An example load curvearchetype may include one that categorizes the user as one that has peakusage in the evening (e.g., “evening peak user”) or one that has peakusage in the morning (e.g., “morning peak user”). The user may then beassociated with an energy use profile (e.g., corresponding to a type ofuser) based on the load curve archetypes that the user's load curvesmost closely resemble. The category of the user may then be used tosegment the user and provide the user with targeted content and/orrecommendations as discussed in FIGS. 4A and 4B.

FIG. 4A illustrates an example of an energy usage alert notification 400provided to a utility customer, according to certain aspects of thesubject technology. The energy usage alert notification 400 includes autility identifier 402, a report analysis 404, a load curve 406, asecond report analysis 408, a time period 410, a neighbor energy graph412, a user energy graph 414, and a recommendation portion 416. Theenergy usage alert notification 400 is provided merely as an example andadditional or fewer features may be included in similar or alternativeformats within the scope of the various embodiments described in thisspecification.

The utility identifier 402 may relate to the utility company associatedwith the generation of the energy usage alert notification 400. Theutility identifier 402 may include a name of the utility company, anaddress for the utility company, and/or contact information for theutility company.

The report analysis 404 may include information relating to how muchenergy usage (e.g., a percentage) for a prior time period (e.g., a year)that occurs within a portion of another time period (e.g., a span ofhours of an evening in a day). Such information indicates to the utilitycustomer that they may fall under a certain category of energy consumer(e.g. a morning peak user, an evening peak user, etc.).

The energy usage alert notification 400 may include additional metricssuch as a chart to provide the utility customer a visual analysis oftheir energy usage. For example, the load curve 406 includes a graphicalrepresentation of the average energy usage or consumption (e.g., inkilowatt-hour as shown) of the utility customer over a time period(e.g., a day) that is aggregated over a longer time period (e.g., ayear). The load curve 406 may be generated in accordance to theembodiments described herein. As further shown, the load curve 406 mayinclude a highlighted portion 407 that graphically indicates an area ofthe load curve in which peak energy usage occurs. In this example, thehighlighted portion 407 corresponds to the hours in the evening (e.g.,6-11 pm) where the peak energy usage occurred for the utility customer.

The second report analysis 408 may include information relating to howthe energy usage of the utility customer compares to other customers forthe time period corresponding to peak energy usage of the utilitycustomer. Such information indicates to the utility customer whether thecustomer is consuming more or less energy that other customers. Theother customers may correspond to one or more neighbors of the utilitycustomer. The time period 410 may correspond to the period of time thatenergy usage is being analyzed for the utility customer and theirneighbors.

Additionally, to facilitate visualization of such energy usage to theutility customer, the neighbor energy graph 412 and the user energygraph 414 are provided. The neighbor energy graph 412 shows a graphicalrepresentation of an amount of energy usage for neighbors of the utilitycustomer, while the user energy graph 414 shows a graphicalrepresentation of an amount of energy usage for the utility customer.Thus, the utility customer is able to easily compare his/her energyusage to the energy usage of their neighbors.

The recommendation portion 416 may include recommendations on how tomodify usage so that the utility customer reduces energy usage duringthe time period corresponding to a period of peak usage as indicated inthe report analysis 404. The recommendations may include set points orset point schedules that may be used on the climate control device,suggestion to tum off light sources and/or electronic devices,maintenance suggestions, and specific adjustments to the climate controldevice, among other types of possible recommendations.

FIG. 4B illustrates an example of an energy usage alert notification 450provided to a utility customer, according to certain aspects of thesubject technology. The energy usage alert notification 450 includes autility identifier 452, a report message 454, a load curve description456, a load curve 458, a report analysis 460, and a second reportmessage 462. The energy usage alert notification 450 is provided merelyas an example and additional or fewer features may be included insimilar or alternative formats within the scope of the variousembodiments described in this specification.

The utility identifier 452 may relate to the utility company associatedwith the generation of the energy usage alert notification 450. Theutility identifier 450 may include a name of the utility company, anaddress for the utility company, and/or contact information for theutility company.

The report message 454 may include information suggesting to the utilitycustomer that they could save money by switching to a differentelectricity rate plan. In an embodiment, providing this information maybe accomplished by: calculating current costs based on current usageinfo (e.g., corresponding to a utility customer's load curve) andcurrent rate plan; calculating costs for one or more alternative rateplans; comparing current cost with the cost of the one or morealternative rate plans; if an alternative rate plan is less, calculatingthe difference and provide the user with the content about how much theutility customer could save.

The energy usage alert notification 450 may include additional metricssuch as a chart to provide the utility customer a visual analysis oftheir energy usage. For example, the load curve 458 includes a graphicalrepresentation of the average energy usage or consumption (e.g., inkilowatt-hour as shown) of the utility customer over a time period(e.g., a day) that is aggregated over a longer time period (e.g., ayear). The load curve description 456 is further provided to indicate tothe utility customer the type of usage data (e.g., average daily use forthe past year) is shown in the load curve 458.

The report analysis 460 indicates that the peak usage of the utilitycustomer occurs during a specific time period (e.g., daytime) and thatmoney could be saved by switching over to a new type of plan instead ofthe current plan. The second report message 462 includes information forswitching over to the new type of plan (e.g., contact information forthe utility), and in an embodiment, may include a hyperlink to anexternal web site for the utility customer to obtain additionalinformation.

FIG. 5 illustrates a flowchart of an example process 500 for the energyusage alert system described in FIG. 1 to provide. The example process500 is provided merely as an example and additional or fewer steps maybe performed in similar or alternative orders, or in parallel, withinthe scope of the various embodiments described in this specification.

At step 502, consumption data for a plurality of users is aggregated. Inan example, the consumption data corresponding with an amount of energyresource consumption for each of the respective plurality of users. Atstep 504, Advanced Metering Infrastructure (AMI) load curves for theplurality of users are generated from the consumption data. In anembodiment, generating AMI load curves for the plurality of usersfurther includes: determining one or more values measuring energyconsumption at specified intervals over a specified time period, each ofthe one or more values at the specified intervals representing anaverage amount of energy usage at a specified interval of the specifiedtime period; determining a total amount of energy usage for thespecified time period; for each value measured at each specifiedinterval over the specified time period, determining a percentage of thetotal amount of energy usage based on the value; and generating agraphical representation of a respective load curve based at least inpart on the percentage of the total amount of energy usage at each ofthe specified intervals over the specified time period. The specifiedtime period may be a day and each of the specified intervals arerespective hours in the day. The values measuring energy consumption atspecified intervals over the specified time period can further representa standard deviation of usage at a respective interval, or a baseload atthe respective interval.

At step 506, a subset of the plurality of users that are peak users thatconsume more resources during a first time period than a second timeperiod are identified from the AMI load curves. The peak users are usersthat consume more resources during evening hours than other hours in aday in an example. At least one AMI curve from the AMI load curvesincludes a peak portion that indicates a higher usage of resourcesduring a portion of a time period in comparison with remaining portionsof the time period. In an embodiment, identifying the subset of theplurality of users that are peak users further comprises: determiningone or more load curve archetypes, each load curve archetype including arespective load curve that represents a type of user based on energyconsumption tracked during a period of time; and using a clusteringalgorithm to segment users into one or more categories based on the AMIload curves and the one or more load curve archetypes. The clusteringalgorithm comprises a k-means algorithm in an example.

At step 508, a plurality of use reports for the peak users are generatedin which each of the use reports provides a visual display of theconsumption data for each of the peak users and energy efficiency advicerelated to consumption during the first time period. At step 510, one ormore of the use reports to one or more of the peak users to reduce theamount of energy resource consumption for the one or more peak users areprovided. Each of the plurality of use reports for the peak usersincludes a graphical representation of a respective load curvecorresponding to a respective peak user.

In another example, a peak user can be identified based on the followingsteps. First, consumption data is aggregated for a plurality of users,the consumption data corresponding with an amount of energy resourceconsumption for each of the respective plurality of users. Load curvesfor the plurality of users are generated from the consumption data. Anew user that is indicated as a peak user is identified based at leastin part on the load curves, the new user being a different user otherthan the plurality of users, the new user consuming more resourcesduring a first time period than a second time period. In an example,identifying the peak user further comprises: determining one or moreload curve archetypes, each load curve archetype including a respectiveload curve that represents a type of user based on energy consumptiontracked during a period of time, and using a clustering algorithm toidentify a respective user as the peak user based on a load curve of thepeak user and one or more categories represented by the one or more loadcurve archetypes.

Further, the steps can include generating a use report for the peak userin which the use report provides a visual display of the consumptiondata for the peak user and energy efficiency advice related toconsumption during the first time period, and providing the use reportto the peak user to reduce the amount of energy resource consumption forthe peak user.

In yet another example, consumption data for a plurality of users may beaggregated, the consumption data corresponding with an amount of energyresource consumption for each of the respective plurality of users.Based on the consumption data, usage patterns for the plurality of usersmay be identified. From the usage patterns, a subset of the plurality ofusers that are peak users that consume more resources during a firsttime period than a second time period is identified. A plurality of usereports for the peak users are generated in which each of the usereports provides a visual display of the consumption data for each ofthe peak users and energy efficiency advice related to consumptionduring the first time period. One or more of the use reports areprovided to one or more of the peak users to reduce the amount of energyresource consumption for the one or more peak users.

FIG. 6 illustrates an example of an environment 600 for implementingaspects in accordance with various embodiments. The environment 600includes a utility company 601, power distribution system 602, utilitycustomer regions 610, 620 and 630, energy usage collector 640, a network650 and a usage alert system 660. The utility customer region 610includes residential structures with corresponding smart meters 611-614.The utility customer region 620 includes commercial structures withcorresponding smart meters 621-623. The utility customer region 630includes multi-family structures with corresponding smart meters631-633. The usage alert system 660 includes a web server 661, anapplication server 662 and a database 663.

The utility company 601 provides a commodity (e.g., electricity, gas,water) to the utility customer regions 610, 620 and 630. The utilitycompany 601 may track the energy usage from each region via a monitoringdevice (e.g., a smart meter) associated with each structure of thecorresponding region. The utility company 601 may receive usage datathat includes the amount of energy consumption (e.g., kWH) for thecorresponding utility account. In an aspect, the utility company 601receives the usage data from the energy usage collector 640 via awireless communication system. In some aspects, the energy usagecollector 640 may obtain the usage data by pulling the usage data fromeach of the smart meter devices. The smart meter devices may broadcastusage data on a periodic or scheduled basis. The utility company 601also may receive the usage data from each monitoring device through awired communication system.

The usage alert system 660 is in communication with the utility company601 via the network 650. The usage alert system 660 may obtain the usagedata from the utility company 601 via the network 650. In an aspect, theusage alert system 660 receives the usage data via the network 650. Theusage alert system 660 may receive the usage data directly from thesmart meter devices.

Each of the utility customer regions 610, 620 and 630 may correspond toa separate geographical location with a respective rate schedule. Insome aspects, an energy usage alert notification for a correspondingutility customer in one region may be generated using usage data ofsimilar users in the same region to provide the corresponding utilitycustomer with a comparative analysis of its energy consumption (e.g.,current energy usage compared to similar customers in the same zip codeor within a certain radius).

The usage alert system 660 also may be in communication with a thirdparty weather service, such as the National Weather Service (not shown).For example, the usage alert system 660 may receive correspondingoutdoor temperatures from the third party weather service via thenetwork 650 (e.g., e-mails, downloaded FTP files, and XML feeds). Inthis respect, the usage alert system 660 may use data from the thirdparty weather service to determine a projected use for a current billingperiod. For example, forecasted weather conditions (e.g., thetemperature, the humidity, the barometric pressure, precipitation, etc.)may indicate that the utility customer's HVAC system is likely to be ingreater use. The usage alert system 660 may estimate the projected usefor the remaining amount of time of the current billing period, andthereby determine if the utility customer is on pace to exceed theprojected bill based on the estimated projected use. In tum, the usagealert system 660 may notify the utility customer through an energy usagealert notification.

The usage alert system 660 communicates the energy usage alertnotification to utility customers associated with the utility customerregions 610, 620 and 630. In some aspects, the usage alert system 660communicates the energy usage alert notification via the network 650.For example, the usage alert system 660 may send the energy usage alertnotification in an e-mail or the utility customer may log into the usagealert system 660 (e.g., the web server 661 and/or application server662) through an associated website to view the disaggregated usage dataincluded in the energy usage alert notification. The usage alert system660 may send the energy usage information to a printing system so thatthe energy usage alert notification can be provided to the utilitycustomer via regular mail (e.g., as part of a utility bill). In otherembodiments, the energy usage information is communicated back to theutility company 601 such that the utility company 601 can provide theenergy usage alert notification to the utility customer.

FIG. 7 illustrates an example of a system 700 for energy usage alerts,according to certain aspects of the subject technology. Although aweb-based environment is described for purposes of explanation,different environments may be used, as appropriate, to implement variousembodiments.

The example system 700 includes a usage alert system 705 and a dataplane 710. The usage alert system 705 includes at least one web server706 and at least one application server 708, as described below. Theusage alert system 705 is an example of an energy usage notificationsystem implemented as computer programs on one or more computers in oneor more locations, in which the systems, components, and techniquesdescribed below can be implemented.

A user can interact with the usage alert system 705 through a clientdevice 702. For example, the client device 702 can be a computer coupledto the usage alert system 705 through a data communication network 704,e.g., the Internet. In some instances, the usage alert system 705 can beimplemented on the client device 702, for example, through a softwareapplication executing on the client device 702. The client device 702generally includes a memory, e.g., a random access memory (RAM), forstoring instructions and data, and a processor for executing storedinstructions. The client device 702 can be any appropriate deviceoperable to send and receive requests, messages, or other types ofinformation over the data communication network 704. The client device702 can also include a display screen though which the user interactingwith the client device 702 can view information, e.g., energy usagealert notification 300 of FIG. 3. Some examples of client devicesinclude personal computers, smart thermostats, cellular phones, handheldmessaging devices, laptop computers, set-top boxes, personal dataassistants, electronic book readers, tablet devices, smartphones and thelike.

The data communication network 704 can include any appropriate network,including an intranet, the Internet, a cellular network, a local areanetwork, a wide area network, or any other such network, or combinationthereof. Components used for such a system can depend at least in partupon the type of network, the environment selected, or both. Protocolsand components for communicating over such a network are well known andwill not be discussed herein in detail. The client device 702 cancommunicate over the data communication network 704 using wired orwireless connections, and combinations thereof.

A user can use the client device 702 to submit a request 720 to log intothe usage alert system 705. The request 720 can request a digital copyof an energy usage alert notification for a corresponding utilityaccount. The energy usage alert notification may include informationrelating to how much energy has been consumed to date and/or a projectedbill amount for a current billing period. The usage alert notificationalso can include information relating to one or more recommendations foradjusting settings in the property associated with the correspondingutility account such that the projected bill is kept below a targetbudget for the current billing period. When the user submits the request720, the request 720 may be transmitted through the data communicationnetwork 704 to the application server 708 within the usage alert system705. The application server 708 responds to the request 720 by using,for example, usage data 712, to identify data 722 describing an energyusage alert with personalized information in response to the request720. The application server 708 sends the data 722 through the datacommunication network 704 to the client device 702 for presentation tothe user.

The data 722 can include data describing a projected bill for a currentbilling period. The data 722 can be used, for example, by the clientdevice 702, to generate a local energy usage alert notification with oneor more interactive features such as energy consumption adjustments withcorresponding utility bill projections and/or instructions for adjustingsettings on a climate control device associated with the correspondingutility customer.

After receiving the data 722 from the application server 708, andthrough the data communication network 704, a software application,e.g., web browser or application 724, running on the client device 702renders an interactive energy usage alert notification using the data722. For example, a pre-bill advisor engine 726 in the application 724can describe the usage to date including a projected use for the currentbilling period, for display on a display screen of the client device702.

In some aspects, the application 724 includes a bill arrival engine 728that is configured to render an interface to the climate control device,and perform one or more actions related to the instructions foradjusting the settings of the climate control device. In someembodiments, the bill arrival engine 728 is configured to obtain datarelating to current settings of the climate control device. The billarrival engine 728 can obtain real-time statistics and/or sensorreadings (e.g., thermometer reading) of current climate conditions inthe property. In an aspect, the application 724 includes an alert engine730 that is configured to render the energy usage alert notificationincluding allow the user to set (or program) rules and/or conditions forreceiving the energy usage alert notification.

In some embodiments, the web server 706, the application server 708, andsimilar components, can be considered to be part of the data plane 710.The handling of all requests and responses, as well as the delivery ofcontent between the client device 702 and the application server 708,can be handled by the web server 706. The web server 706 and theapplication server 708 are merely example components. However, more orfewer components can be used as structured code can be executed on anyappropriate device or host machine as discussed elsewhere herein.

The data plane 710 includes one or more resources, servers, hosts,instances, routers, switches, data stores, other similar components, ora combination thereof. The resources of the data plane 710 are notlimited to storing and providing access to data. Indeed, there may beseveral servers, layers, or other elements, processes, or components,which may be chained or otherwise configured, and which can interact toperform tasks including, for example, obtaining data from an appropriatedata store. In some embodiments, the term “data store” refers to anydevice or combination of devices capable of storing, accessing, andretrieving data, which may include any combination and number of dataservers, databases, data storage devices, and data storage media, in anystandard, distributed, or clustered environment.

The data stores of the data plane 710 can include several separate datatables, databases, or other data storage mechanisms and media forstoring data relating to a particular aspect. For example, the dataplane 710 illustrated includes mechanisms for storing usage data 712 anduser information 716, which can be used to generate the energy usagealert notification. The data plane 710 is also shown to include amechanism for storing similar user data 714, which can be used forpurposes such as reporting a comparative analysis of the usage data forthe corresponding utility customer. The data plane 710 is operable,through logic associated therewith, to receive instructions from theapplication server 708 and to obtain, update, or otherwise process data,instructions, or other such information in response thereto, asdescribed above.

Each server typically includes an operating system that providesexecutable program instructions for the general administration andoperation of that server, and typically will include a computer-readablemedium storing instructions that, when executed by a processor of theserver, enable the server to perform its intended functions. Suitableimplementations for the operating system and general functionality ofthe servers are known or commercially available, and are readilyimplemented by persons having ordinary skill in the art, particularly inlight of the disclosure herein.

The environment m one embodiment is a distributed computing environmentincluding several computer systems and components that areinterconnected through one or more communication links, using one ormore computer networks or direct connections. However, the systemdescribed above can be configured to operate equally well using fewer ora greater number of components than are illustrated in FIG. 7. Thus, thesystem 700 in FIG. 7 is provided merely as one example, and does notlimit the scope of the disclosure.

FIG. 8 illustrates an example configuration of components of a computingdevice 800, e.g., the climate control devices 103 a-103 n of FIG. 1,according to certain aspects of the subject technology. In this example,the computing device 800 includes a processor 802 for executinginstructions that can be stored in a memory device or element 804. Theinstructions may cause the computing device 800 to execute acomputer-implemented method for processing energy usage alerts from theenergy usage alert system 100 (FIG. 1) and/or receive instructions toautomatically adjust settings (e.g., temperature settings, alarmsettings, power settings) of the client computing device 800. As wouldbe apparent to one of ordinary skill in the art, the computing device800 can include many types of memory, data storage, or non-transitorycomputer-readable storage media, such as a first data storage forprogram instructions for execution by the processor 802, a separatestorage for usage history or user information, a removable memory forsharing information with other devices, etc. In some embodiments, thecomputing device 800 can include one or more communication components806, such as a Wi-Fi, Bluetooth®, radio frequency, near-fieldcommunication, wired, or wireless communication system. The computingdevice 800 in many embodiments can communicate with a network, such asthe Internet, and may be able to communicate with other such devices(e.g., the energy usage alert system 100, other climate controldevices). As discussed, the computing device 800 in many embodimentswill include at least one input element 808 able to receive conventionalinput from a user. This conventional input can include, for example, apush button, touch pad, touch screen, wheel, joystick, keyboard, mouse,keypad, or any other such device or element whereby a user can input acommand to the device. In some embodiments, however, such a device mightnot include any buttons at all, and might be controlled only through acombination of visual and audio commands, such that a user can controlthe device without having to be in contact with the device. Thecomputing device 800 includes some type of display element 810, such asa touch screen or liquid crystal display (LCD).

The various embodiments can be implemented m a wide variety of operatingenvironments, which in some cases can include one or more usercomputers, computing devices, or processing devices which can be used tooperate any of a number of applications. User or client devices caninclude any of a number of general purpose personal computers, such asdesktop or laptop computers running a standard operating system, as wellas cellular, wireless, and handheld devices running mobile software andcapable of supporting a number of networking and messaging protocols.Such a system also can include a number of workstations running any of avariety of commercially-available operating systems and other knownapplications for purposes such as development and database management.These devices also can include other electronic devices, such as dummyterminals, thin-clients, gaming systems, and other devices capable ofcommunicating via a network.

Various aspects also can be implemented as part of at least one serviceor Web service, such as may be part of a service-oriented architecture.Services such as Web services can communicate using any appropriate typeof messaging, such as by using messages in extensible markup language(XML) format and exchanged using an appropriate protocol such as SOAP(derived from the “Simple Object Access Protocol”). Processes providedor executed by such services can be written in any appropriate language,such as the Web Services Description Language (WSDL). Using a languagesuch as WSDL allows for functionality such as the automated generationof client-side code in various SOAP frameworks.

Most embodiments utilize at least one network that would be familiar tothose skilled in the art for supporting communications using any of avariety of commercially-available protocols, such as TCP/IP, OSI, FTP,UPnP, NFS, and CIFS. The network can be, for example, a local areanetwork, a wide-area network, a virtual private network, the Internet,an intranet, an extranet, a public switched telephone network, aninfrared network, a wireless network, and any combination thereof.

In embodiments utilizing a Web server, the Web server can run any of avariety of server or mid-tier applications, including HTTP servers, FTPservers, CGI servers, data servers, Java servers, and business mapservers. The server(s) also may be capable of executing programs orscripts in response requests from user devices, such as by executing oneor more Web applications that may be implemented as one or more scriptsor programs written in any programming language, such as Java®, C, C# orC++, or any scripting language, such as Perl, Python, or TCL, as well ascombinations thereof. The server(s) may also include database servers,including without limitation those commercially available from Oracle®,Microsoft®, Sybase®, and IBM®.

The environment can include a variety of data stores and other memoryand storage media as discussed above. These can reside in a variety oflocations, such as on a storage medium local to (and/or resident in) oneor more of the computers or remote from any or all of the computersacross the network. In a particular set of embodiments, the informationmay reside in a storage-area network (“SAN”) familiar to those skilledin the art. Similarly, any necessary files for performing the functionsattributed to the computers, servers, or other network devices may bestored locally and/or remotely, as appropriate. Where a system includescomputerized devices, each such device can include hardware elementsthat may be electrically coupled via a bus, the elements including, forexample, at least one central processing unit (CPU), at least one inputdevice (e.g., a mouse, keyboard, controller, touch screen, or keypad),and at least one output device (e.g., a display device, printer, orspeaker). Such a system may also include one or more storage devices,such as disk drives, optical storage devices, and solid-state storagedevices such as random access memory (“RAM”) or read-only memory(“ROM”), as well as removable media devices, memory cards, flash cards,etc.

Such devices also can include a computer-readable storage media reader,a communications device (e.g., a modem, a network card (wireless orwired), an infrared communication device, etc.), and working memory asdescribed above. The computer-readable storage media reader can beconnected with, or configured to receive, a computer-readable storagemedium, representing remote, local, fixed, and/or removable storagedevices as well as storage media for temporarily and/or more permanentlycontaining, storing, transmitting, and retrieving computer-readableinformation. The system and various devices also typically will includea number of software applications, modules, services, or other elementslocated within at least one working memory device, including anoperating system and application programs, such as a client applicationor Web browser. It should be appreciated that alternate embodiments mayhave numerous variations from that described above. For example,customized hardware might also be used and/or particular elements mightbe implemented in hardware, software (including portable software, suchas applets), or both. Further, connection to other computing devicessuch as network input/output devices may be employed.

Storage media and computer readable media for containing code, orportions of code, can include any appropriate media known or used in theart, including storage media and communication media, such as but notlimited to volatile and non-volatile, removable and non-removable mediaimplemented in any method or technology for storage and/or transmissionof information such as computer readable instructions, data structures,program modules, or other data, including RAM, ROM, EEPROM, flash memoryor other memory technology, CD-ROM, digital versatile disk (DVD) orother optical storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe a system device. Based on the disclosure and teachings providedherein, a person of ordinary skill in the art will appreciate other waysand/or methods to implement the various embodiments.

The specification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense. It will, however, beevident that various modifications and changes may be made thereuntowithout departing from the broader spirit and scope of the disclosure asset forth in the claims.

The description of the subject technology is provided to enable anyperson skilled in the art to practice the various embodiments describedherein. While the subject technology has been particularly describedwith reference to the various figures and embodiments, it should beunderstood that these are for illustration purposes only and should notbe taken as limiting the scope of the subject technology.

There may be many other ways to implement the subject technology.Various functions and elements described herein may be partitioneddifferently from those shown without departing from the scope of thesubject technology. Various modifications to these embodiments will bereadily apparent to those skilled in the art, and generic principlesdefined herein may be applied to other embodiments. Thus, many changesand modifications may be made to the subject technology, by one havingordinary skill in the art, without departing from the scope of thesubject technology.

A reference to an element in the singular is not intended to mean “oneand only one” unless specifically stated, but rather “one or more.” Theterm “some” refers to one or more. Underlined and/or italicized headingsand subheadings are used for convenience only, do not limit the subjecttechnology, and are not referred to in connection with theinterpretation of the description of the subject technology. Allstructural and functional equivalents to the elements of the variousembodiments described throughout this disclosure that are known or latercome to be known to those of ordinary skill in the art are expresslyincorporated herein by reference and intended to be encompassed by thesubject technology. Moreover, nothing disclosed herein is intended to bededicated to the public regardless of whether such disclosure isexplicitly recited in the above description.

What is claimed is:
 1. A computer-implemented method performed by acomputer including at least a processor and a memory, the methodcomprising: aggregating, by the processor of the computer, consumptiondata for a plurality of users, the consumption data corresponding withan amount of energy resource consumption for each of the plurality ofusers; generating, by the processor from the consumption data, a loadcurve for each of the plurality of users; identifying, by the processorfrom the load curves, a set of target users from the plurality of usersthat consume more resources during an evening time period during a day,wherein the identifying comprises: comparing the load curve of each ofthe plurality of users to a plurality of load curve archetypes, whereinthe plurality of load curve archetypes includes at least (i) a firstarchetype that categorizes the user as an evening peak user that has apeak usage in the evening time period and (ii) a second archetype thatcategorizes the user as a morning peak user that has the peak usage in amorning time period; and generating the set of target users from theplurality of users that are identified as the evening peak users basedon at least comparing the load curves; selecting, by the processoraccessing a data structure, a first recommendation from a plurality ofavailable recommendations for the evening peak users; modifying, by theprocessor, the first recommendation to include an instruction forcausing an action to be performed to adjust a setting in a climatecontrol device to reduce resource consumption during the evening timeperiod; and controlling, by the processor over a computer network basedon the set of target users, transmission of each of the firstrecommendation in electronic form to remote devices associated withcorresponding target users to cause the evening peak user to adjust theclimate control device to reduce resource consumption during the eveningtime period.
 2. The computer-implemented method of claim 1, whereincomparing the load curve includes: associating each of the plurality ofusers as the evening peak user or the morning peak user based on theload curve archetype that the user's load curve most closely resembles.3. The computer-implemented method of claim 1, further comprising:modifying the first recommendation with a suggestion to switch from acurrent electricity rate plan to a different electricity rate plan basedupon a load curve of a target user and rate plan data of electricityrate plans.
 4. The computer-implemented method of claim 1, whereingenerating load curves for the plurality of users further comprises:determining one or more values measuring energy consumption at specifiedintervals over a specified time period, each of the one or more valuesat the specified intervals representing an average amount of energyusage at a specified interval of the specified time period; determininga total amount of energy usage for the specified time period; for eachvalue measured at each specified interval over the specified timeperiod, determining a percentage of the total amount of energy usagebased on the value; and generating a graphical representation of arespective load curve based at least in part on the percentage of thetotal amount of energy usage at each of the specified intervals over thespecified time period.
 5. The computer-implemented method of claim 4,wherein the values measuring energy consumption at specified intervalsover the specified time period further represent a standard deviation ofusage at a respective interval, or a baseload at the respectiveinterval.
 6. The computer-implemented method of claim 1, wherein theidentifying a set of target users further comprises: using a clusteringalgorithm to segment users into one or more categories based on the loadcurves and the plurality of load curve archetypes.
 7. Thecomputer-implemented method of claim 6, wherein the one or morecategories comprise at least one of the evening peak user, the morningpeak user, a mid-day peak user, or a peak user corresponding to a seasonwithin a year.
 8. The computer-implemented method of claim 1, furthercomprising: generating, by the processor, a use report in electronicform for each target user in the set of target users, wherein the usereport for a target user provides a visual display of the consumptiondata for the target user; and modifying the use report to include thefirst recommendation for each target user; wherein the use reportincluding the first recommendation is transmitted in electronic form tothe remote devices associated with the corresponding target user.
 9. Thecomputer-implemented method of claim 1, comprising: determining, by theprocessor, that a target user owns an electric vehicle based upon theload curve of the target user; and modifying, by the processor, thefirst recommendation to further include a suggestion for charging theelectric vehicle at a particular time.
 10. A computing device forimplementing an evening peak user campaign, the computing devicecomprising: at least one processor; and memory storing instructionsthat, when executed by the at least one processor, cause the computingdevice to: aggregate consumption data for a plurality of users, theconsumption data corresponding with an amount of energy resourceconsumption for each of the plurality of users; generate, from theconsumption data, a load curve for each of the plurality of users;identifying, from the load curves, a set of target users from theplurality of users that consume more resources during an evening timeperiod during a day, wherein the identifying comprises: comparing theload curve of each of the plurality of users to a plurality of loadcurve archetypes, wherein the plurality of load curve archetypesincludes at least (i) a first archetype that categorizes the user as anevening peak user that has a peak usage in the evening time period and(ii) a second archetype that categorizes the user as a morning peak userthat has the peak usage in a morning time period; and generating the setof target users from the plurality of users that are identified as theevening peak users based on at least comparing the load curves; select,by accessing a data structure, a first recommendation from a pluralityof available recommendations for the evening peak users; modify thefirst recommendation to include an instruction for causing an action tobe performed to adjust a setting in a climate control device to reduceresource consumption during the evening time period; and transmit, overa computer network, the first recommendation in electronic form toremote devices associated with corresponding target users to cause theevening peak user to adjust the climate control device to reduceresource consumption during the evening time period.
 11. The computingdevice of claim 10, wherein a target user consumes more resources duringevening hours than other hours in a day, and wherein the instructionscause the computing device to: determine that a target user owns anelectric vehicle based upon a load curve of the target user; modify thefirst recommendation to include energy efficiency advice for chargingthe electric vehicle at a particular time.
 12. The computing device ofclaim 10, wherein at least one load curve from the load curves includesa peak portion that indicates a higher usage of resources during aportion of a time period in comparison with remaining portions of thetime period.
 13. The computing device of claim 10, wherein to generateload curves for the plurality of users further comprises instructionsthat, when executed by the at least one processor, cause the computingdevice to: determine one or more values measuring energy consumption atspecified intervals over a specified time period, each of the one ormore values at the specified intervals representing an average amount ofenergy usage at a specified interval of the specified time period;determine a total amount of energy usage for the specified time period;for each value measured at each specified interval over the specifiedtime period, determine a percentage of the total amount of energy usagebased on the value; and generate a graphical representation of arespective load curve based at least in part on the percentage of thetotal amount of energy usage at each of the specified intervals over thespecified time period.
 14. The computing device of claim 13, wherein thevalues measuring energy consumption at specified intervals over thespecified time period further represent a standard deviation of usage ata respective interval, or a baseload at the respective interval.
 15. Thecomputing device of claim 10, further comprising instructions that, whenexecuted by the at least one processor, cause the computing device to:generate a use report in electronic form for each target user in the setof target users, wherein the use report for a target user provides avisual display of the consumption data for the target user; and modifythe use report to include the first recommendation for each target user;wherein the use report including the first recommendation is transmittedin electronic form to the remote devices associated with thecorresponding target user.
 16. The computing device of claim 10, whereinthe identifying a set of target users user further comprises: using aclustering algorithm to identify a respective user as a target user forinclusion within the set of target users based on a load curve of thetarget user and one or more categories represented by the plurality ofload curve archetypes.
 17. The computing device of claim 16, wherein theone or more categories comprise at least one of the evening peak user,the morning peak user, a mid-day peak user, or a peak user correspondingto a season within a year.
 18. A non-transitory computer-readable mediumincluding stored instructions that, when executed by at least oneprocessor of a computing device, cause the computing device to:aggregate consumption data for a plurality of users, the consumptiondata corresponding with an amount of energy resource consumption foreach of the plurality of users; generate, from the consumption data, aload curve for each of the plurality of users; identifying, from theload curves, a set of target users from the plurality of users thatconsume more resources during an evening time period during a day,wherein the identifying comprises: comparing the load curve of each ofthe plurality of users to a plurality of load curve archetypes, whereinthe plurality of load curve archetypes includes at least (i) a firstarchetype that categorizes the user as an evening peak user that has apeak usage in the evening time period and (ii) a second archetype thatcategorizes the user as a morning peak user that has the peak usage in amorning time period; and generating the set of target users from theplurality of users that are identified as the evening peak users basedon at least comparing the load curves; select, by accessing a datastructure, a first recommendation from a plurality of availablerecommendations for the evening peak users; modify the firstrecommendation to include an instruction for causing an action to beperformed to adjust a setting in a climate control device to reduceresource consumption during the evening time period; and transmit, overa computer network, the first recommendation in electronic form toremote devices associated with corresponding target users to cause theevening peak user to adjust the climate control device to reduceresource consumption during the evening time period.